With recent advances in digital technology, a radiographic image is transformed into a digital image signal, which is displayed on a CRT or the like or printed out upon being subjected to image processing. In addition, recently, a diagnosis support apparatus which automatically extracts a tumor shadow or the like from a radiographic image has been developed. An image containing the tumor shadow or the like extracted by the diagnosis support apparatus is often displayed on a film or CRT.
Such methods of automatically extracting isolated shadows include, for example, the isolated shadow extraction method disclosed in U.S. Pat. No. 4,907,156. This method will be described briefly. A differential image between a tumor shadow enhanced image and a tumor shadow suppressed image is generated. Multiple threshold processing is performed for the generated differential image, and known labeling processing is simultaneously performed, thereby calculating a feature amount such as a roundness from an isolated shadow having a value that is equal to or larger than a predetermined threshold and extracting an isolated shadow on the basis of the feature amount at the same time.
As another method of automatically extracting isolated shadows, the following extraction method is disclosed in Japanese Patent No. 2,571,132. A component of a normalized gradient ∇fij/|∇fij| of image data fij of each pixel Pij (i=1, 2, . . . , 8; j=1, 2, 3) in the direction of a line segment Li is obtained by ∇fij/|∇fij|*ei (where ei represents a unit vector extending from each pixel Pij to a predetermined pixel P0, and * represents the inner product). (Note that the unit vectors ei are those extending from the pixel P0 in eight directions including horizontal directions, vertical directions, and 45° directions.)
Then, assuming that the inward direction (the direction toward the predetermined pixel P0) of the component is positive, and the outward direction is negative, the maximum value of each line segment Li (i=1, 2, . . . , 8) is obtained as follows:{∇fij/|∇fij|*ei}M (i=1, 2, . . . , 8)In addition, the sum of these maximum values {∇fij/|∇fij|*ei}M is obtained. This sum is compared as a feature value C2 with a predetermined threshold Th2. Depending on whether C2≧Th2 or C2<Th2, it is determined whether or not the predetermined pixel P0 is a pixel in each tumor shadow. In addition, a quoit like morphological filter used to extract an isolated shadow is described in “Study on Automatic Lung Cancer Lesion Recognition Using 3D Morphological Filter” (Masato Nakayama et al., Proceedings of Japanese Society of Medical Imaging Technology 95, pp. 155-16 (1995). This technique uses a Q filter expressed by a combination of a D filter (Disk Filter) having a radius ri and an R filter (Ring Filter) having inner radii r2 and r3. This transform is called Q transform. More specifically, Q transform is the processing of subtracting the pixel value obtained after Dilation using the R filter from the pixel value obtained after Dilation using the D filter. The processing of performing Q transform of an image after Q transform will be referred to as inverse Q transform. The relationship between Q transform and inverse Q transform is similar to that between Fourier transform and inverse Fourier transform. This is because, Q transform can be regarded as a process of extracting a Q filter component in an image, and inverse Q transform can be regarded as a process of inversely transforming the extracted component to express it in the original image space.
The following is the definition expression of the Q filter. If the D filter (Disk Filter) D(x, y) and R filter (Ring Filter) R(x, y) are represented byD(x, y)=0: for x2+y2≦r12_∞: for others  (1)R(x, y)=0: for r22≦x2+y2≦r32_∞: for others  (2)then, Q transform is represented byG(x, y)=f(x, y)ΘD(x, y)−f(x, y)ΘR(x, y)  (3)where Θ represents Dilation. When an original image is represented by f(x, y), and a filter function is given by h(x, y), thenf(x, y)Θh(x, y)=max{f(x+x1, y+y1)+h(x1, y1) |(x1, y1)εK}  (4)where K is the domain of the filter function.
According to the inventions disclosed in U.S. Pat. No. 4,907,156 and Japanese Patent No. 2,571,132, isolated shadows can be enhanced. However, for example, in a chest image, images of regions other than an isolated shadow, e.g., edge portions such as a vomer shadow, are also enhanced. Consequently, not all regions with pixel values equal to or more than a predetermined value on an image after enhancement do indicate isolated shadow regions. In order to extract only an isolated shadow, therefore, some kind of feature extraction processing must be done with respect to an enhanced image.
In such processing, when a vomer shadow and tumor shadow are located near each other or they overlap, it is difficult to separate the tumor shadow from the vomer shadow, resulting in a deterioration in tumor extraction precision. In the invention disclosed in Japanese Patent No. 2,571,132, in particular, if the pixel value gradient of an entire region in which a tumor exists is stronger than that of a tumor shadow, a normalized vector representing the tumor shadow cannot be properly calculated, resulting in a deterioration in tumor shadow extraction precision. This raises a problem in extracting a tumor shadow existing near the periphery of the lung field from, for example, a chest frontal image.
When a quoit-like morphological filter disclosed in “Study on Automatic Lung Cancer Lesion Recognition Using 3D Morphological Filter” is used, only an isolated shadow can be extracted. Therefore, feature amount extraction processing and the like need not be performed for isolated shadow extraction. In addition, even if a vomer shadow and tumor shadow overlap, only the tumor shadow can be extracted.
In practice, however, tumor shadows rarely exist in a bulging state, and tumor portions generally blend as images with surrounding images like stains. For this reason, a quoit-like morphological filter is not suited for the extraction of an actual tumor shadow existing in, for example, a chest image. This is because a quoit-like morphological filter works well only when a tumor shadow bulges from surrounding images. This raises a problem in extracting a tumor shadow existing near the periphery of the lung field in, for example, a chest frontal image.